def test(netFile, dataSet, model='RNN', trees=None):
if trees == None:
trees = tr.loadTrees(dataSet)
assert netFile is not None, "Must give model to test"
print "Testing netFile %s" % netFile
with open(netFile, 'r') as fid:
opts = pickle.load(fid)
_ = pickle.load(fid)
if (model == 'RNTN'):
nn = RNTN(opts.wvecDim, opts.outputDim, opts.numWords,
opts.minibatch)
elif (model == 'RNN'):
nn = RNN(opts.wvecDim, opts.outputDim, opts.numWords,
opts.minibatch)
elif (model == 'RNN2'):
nn = RNN2(opts.wvecDim, opts.middleDim, opts.outputDim,
opts.numWords, opts.minibatch)
elif (opts.model == 'RNN3'):
nn = RNN3(opts.wvecDim, opts.middleDim, opts.outputDim,
opts.numWords, opts.minibatch)
elif (model == 'DCNN'):
nn = DCNN(opts.wvecDim,
opts.ktop,
opts.m1,
opts.m2,
opts.n1,
opts.n2,
0,
opts.outputDim,
opts.numWords,
2,
opts.minibatch,
rho=1e-4)
trees = cnn.tree2matrix(trees)
else:
raise '%s is not a valid neural network so far only RNTN, RNN, RNN2, RNN3, and DCNN' % opts.model
nn.initParams()
nn.fromFile(fid)
print "Testing %s..." % model
cost, correct, guess, total = nn.costAndGrad(trees, test=True)
correct_sum = 0
for i in xrange(0, len(correct)):
correct_sum += (guess[i] == correct[i])
# TODO
# Plot the confusion matrix?
conf_arr = np.zeros((5, 5))
for i in xrange(0, len(correct)):
current_correct = correct[i]
current_guess = guess[i]
conf_arr[current_correct][current_guess] += 1.0
makeconf(conf_arr, model, dataSet)
print "Cost %f, Acc %f" % (cost, correct_sum / float(total))
return correct_sum / float(total)
def test(netFile, dataSet, model='RNN', trees=None):
if trees == None:
trees = tr.loadTrees(dataSet)
assert netFile is not None, "Must give model to test"
print "Testing netFile %s" % netFile
opts = None
with open(netFile, 'r') as fid:
opts = pickle.load(fid)
_ = pickle.load(fid)
if (model == 'RNTN'):
nn = RNTN(opts.wvecDim, opts.outputDim, opts.numWords,
opts.minibatch)
elif (model == 'RNN'):
nn = RNN(opts.wvecDim, opts.outputDim, opts.numWords,
opts.minibatch)
elif (model == 'RNN2'):
nn = RNN2(opts.wvecDim, opts.middleDim, opts.outputDim,
opts.numWords, opts.minibatch)
elif (opts.model == 'RNN3'):
nn = RNN3(opts.wvecDim, opts.middleDim, opts.outputDim,
opts.numWords, opts.minibatch)
elif (model == 'DCNN'):
nn = DCNN(opts.wvecDim,
opts.ktop,
opts.m1,
opts.m2,
opts.n1,
opts.n2,
0,
opts.outputDim,
opts.numWords,
2,
opts.minibatch,
rho=1e-4)
trees = cnn.tree2matrix(trees)
else:
raise '%s is not a valid neural network so far only RNTN, RNN, RNN2, RNN3, and DCNN' % opts.model
nn.initParams()
nn.fromFile(fid)
print "Testing %s..." % model
cost, correct, guess, total = nn.costAndGrad(trees, test=True)
correct_sum = 0
for i in xrange(0, len(correct)):
correct_sum += (guess[i] == correct[i])
cm = confusion_matrix(correct, guess)
makeconf(cm)
plt.savefig("plots/" + opts.model + "/confusion_matrix_" + model +
"wvecDim_" + str(opts.wvecDim) + "_middleDim_" +
str(opts.middleDim) + ".png")
print "Cost %f, Acc %f" % (cost, correct_sum / float(total))
return correct_sum / float(total)
def run(args=None):
usage = "usage : %prog [options]"
parser = optparse.OptionParser(usage=usage)
parser.add_option("--test",
action="store_true",
dest="test",
default=False)
# Optimizer
parser.add_option("--minibatch", dest="minibatch", type="int", default=30)
parser.add_option("--optimizer",
dest="optimizer",
type="string",
default="adagrad")
parser.add_option("--epochs", dest="epochs", type="int", default=50)
parser.add_option("--step", dest="step", type="float", default=1e-2)
parser.add_option("--middleDim", dest="middleDim", type="int", default=10)
parser.add_option("--outputDim", dest="outputDim", type="int", default=5)
parser.add_option("--wvecDim", dest="wvecDim", type="int", default=30)
# for DCNN only
parser.add_option("--ktop", dest="ktop", type="int", default=5)
parser.add_option("--m1", dest="m1", type="int", default=10)
parser.add_option("--m2", dest="m2", type="int", default=7)
parser.add_option("--n1", dest="n1", type="int", default=6)
parser.add_option("--n2", dest="n2", type="int", default=12)
parser.add_option("--outFile",
dest="outFile",
type="string",
default="models/test.bin")
parser.add_option("--inFile",
dest="inFile",
type="string",
default="models/test.bin")
parser.add_option("--data", dest="data", type="string", default="train")
parser.add_option("--model", dest="model", type="string", default="RNN")
(opts, args) = parser.parse_args(args)
# make this false if you dont care about your accuracies per epoch, makes things faster!
evaluate_accuracy_while_training = True
# Testing
if opts.test:
test(opts.inFile, opts.data, opts.model)
return
print "Loading data..."
train_accuracies = []
dev_accuracies = []
# load training data
trees = tr.loadTrees('train')
opts.numWords = len(tr.loadWordMap())
if (opts.model == 'RNTN'):
nn = RNTN(opts.wvecDim, opts.outputDim, opts.numWords, opts.minibatch)
elif (opts.model == 'RNN'):
nn = RNN(opts.wvecDim, opts.outputDim, opts.numWords, opts.minibatch)
elif (opts.model == 'RNN2'):
nn = RNN2(opts.wvecDim, opts.middleDim, opts.outputDim, opts.numWords,
opts.minibatch)
elif (opts.model == 'RNN3'):
nn = RNN3(opts.wvecDim, opts.middleDim, opts.outputDim, opts.numWords,
opts.minibatch)
elif (opts.model == 'DCNN'):
nn = DCNN(opts.wvecDim,
opts.ktop,
opts.m1,
opts.m2,
opts.n1,
opts.n2,
0,
opts.outputDim,
opts.numWords,
2,
opts.minibatch,
rho=1e-4)
trees = cnn.tree2matrix(trees)
else:
raise '%s is not a valid neural network so far only RNTN, RNN, RNN2, RNN3, and DCNN' % opts.model
nn.initParams()
sgd = optimizer.SGD(nn,
alpha=opts.step,
minibatch=opts.minibatch,
optimizer=opts.optimizer)
dev_trees = tr.loadTrees("dev")
for e in range(opts.epochs):
start = time.time()
print "Running epoch %d" % e
sgd.run(trees)
end = time.time()
print "Time per epoch : %f" % (end - start)
with open(opts.outFile, 'w') as fid:
pickle.dump(opts, fid)
pickle.dump(sgd.costt, fid)
nn.toFile(fid)
if evaluate_accuracy_while_training:
print "testing on training set real quick"
train_accuracies.append(
test(opts.outFile, "train", opts.model, trees))
print "testing on dev set real quick"
dev_accuracies.append(
test(opts.outFile, "dev", opts.model, dev_trees))
# clear the fprop flags in trees and dev_trees
for tree in trees:
tr.leftTraverse(tree.root, nodeFn=tr.clearFprop)
for tree in dev_trees:
tr.leftTraverse(tree.root, nodeFn=tr.clearFprop)
print "fprop in trees cleared"
if evaluate_accuracy_while_training:
pdb.set_trace()
print train_accuracies
print dev_accuracies
def run(args=None):
usage = "usage : %prog [options]"
parser = optparse.OptionParser(usage=usage)
parser.add_option("--test",
action="store_true",
dest="test",
default=False)
# Optimizer
parser.add_option("--minibatch", dest="minibatch", type="int", default=30)
parser.add_option("--optimizer",
dest="optimizer",
type="string",
default="adagrad")
parser.add_option("--epochs", dest="epochs", type="int", default=50)
parser.add_option("--step", dest="step", type="float", default=1e-2)
parser.add_option("--middleDim", dest="middleDim", type="int", default=10)
parser.add_option("--outputDim", dest="outputDim", type="int", default=5)
parser.add_option("--wvecDim", dest="wvecDim", type="int", default=30)
# By @tiagokv, just to ease the first assignment test
parser.add_option("--wvecDimBatch",
dest="wvecDimBatch",
type="string",
default="")
# for DCNN only
parser.add_option("--ktop", dest="ktop", type="int", default=5)
parser.add_option("--m1", dest="m1", type="int", default=10)
parser.add_option("--m2", dest="m2", type="int", default=7)
parser.add_option("--n1", dest="n1", type="int", default=6)
parser.add_option("--n2", dest="n2", type="int", default=12)
parser.add_option("--outFile",
dest="outFile",
type="string",
default="models/test.bin")
parser.add_option("--inFile",
dest="inFile",
type="string",
default="models/test.bin")
parser.add_option("--data", dest="data", type="string", default="train")
parser.add_option("--model", dest="model", type="string", default="RNN")
(opts, args) = parser.parse_args(args)
# make this false if you dont care about your accuracies per epoch, makes things faster!
evaluate_accuracy_while_training = True
# Testing
if opts.test:
test(opts.inFile, opts.data, opts.model)
return
print "Loading data..."
train_accuracies = []
dev_accuracies = []
# load training data
trees = tr.loadTrees('train')
opts.numWords = len(tr.loadWordMap())
if (opts.model == 'RNTN'):
nn = RNTN(opts.wvecDim, opts.outputDim, opts.numWords, opts.minibatch)
elif (opts.model == 'RNN'):
nn = RNN(opts.wvecDim, opts.outputDim, opts.numWords, opts.minibatch)
elif (opts.model == 'RNN2'):
nn = RNN2(opts.wvecDim, opts.middleDim, opts.outputDim, opts.numWords,
opts.minibatch)
elif (opts.model == 'RNN3'):
nn = RNN3(opts.wvecDim, opts.middleDim, opts.outputDim, opts.numWords,
opts.minibatch)
elif (opts.model == 'DCNN'):
nn = DCNN(opts.wvecDim,
opts.ktop,
opts.m1,
opts.m2,
opts.n1,
opts.n2,
0,
opts.outputDim,
opts.numWords,
2,
opts.minibatch,
rho=1e-4)
trees = cnn.tree2matrix(trees)
else:
raise '%s is not a valid neural network so far only RNTN, RNN, RNN2, RNN3, and DCNN' % opts.model
nn.initParams()
sgd = optimizer.SGD(nn,
alpha=opts.step,
minibatch=opts.minibatch,
optimizer=opts.optimizer)
# assuring folder for plots exists
if (os.path.isdir('plots') == False): os.makedirs('test')
if (os.path.isdir('plots/' + opts.model) == False):
os.makedirs('plots/' + opts.model)
dev_trees = tr.loadTrees("dev")
for e in range(opts.epochs):
start = time.time()
print "Running epoch %d" % e
sgd.run(trees)
end = time.time()
print "Time per epoch : %f" % (end - start)
with open(opts.outFile, 'w') as fid:
pickle.dump(opts, fid)
pickle.dump(sgd.costt, fid)
nn.toFile(fid)
if evaluate_accuracy_while_training:
print "testing on training set real quick"
train_accuracies.append(
test(opts.outFile, "train", opts.model, trees))
print "testing on dev set real quick"
dev_accuracies.append(
test(opts.outFile, "dev", opts.model, dev_trees))
# clear the fprop flags in trees and dev_trees
for tree in trees:
tr.leftTraverse(tree.root, nodeFn=tr.clearFprop)
for tree in dev_trees:
tr.leftTraverse(tree.root, nodeFn=tr.clearFprop)
print "fprop in trees cleared"
if evaluate_accuracy_while_training:
#pdb.set_trace()
plt.figure()
#Lets set up the plot
plt.title('Accuracy in set per epochs')
plt.plot(range(opts.epochs), train_accuracies, label='train')
plt.plot(range(opts.epochs), dev_accuracies, label='dev')
with open('dev_accu' + opts.model, 'a') as fid:
fid.write(
str(opts.wvecDim) + ',' + str(opts.middleDim) + ',' +
str(dev_accuracies[-1]) + ';')
#plt.axis([0,opts.epochs,0,1])
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.legend(loc=2, borderaxespad=0.)
#always save with middleDim, even if it's a one-layer RNN
plt.savefig('plots/' + opts.model + '/accuracy_wvec_' +
str(opts.wvecDim) + '_middleDim_' + str(opts.middleDim) +
' .png')
print 'image saved at %s' % os.getcwd()